1
INDIVIDUALS’ DECISIONS IN THE PRESENCE OF MULTIPLE GOALS
Benedict G.C.
Marketing section, Department of Business Economics, Erasmus School
Dellaert*
of Economics, Erasmus University Rotterdam,
PO Box 1738, 3000 DR Rotterdam, The Netherlands,
[email protected], phone: +31 10 4081301
Joffre Swait
Institute for Choice, University of South Australia
[email protected]
Wiktor L. (Vic)
Department of Resource Economics and Environmental Sociology,
Adamowicz
University of Alberta, [email protected]
Theo A. Arentze
Urban Systems and Real Estate group, Eindhoven University of
Technology
Elizabeth E. Bruch
Department of Sociology, University of Michigan
Elisabetta Cherchi
Transport Operations Research Group, Newcastle University
Caspar Chorus
Transport and Logistics group, Delft University of Technology
Bas Donkers
Marketing section, Department of Business Economics, Erasmus School
of Economics, Erasmus University Rotterdam
Fred M. Feinberg
Ross School of Business and Department of Statistics, University of
Michigan
A.A.J. Marley
Institute for Choice, University of South Australia and Department of
Psychology, University of Victoria
Linda Court
Carroll School of Management, Boston College
Salisbury
*Corresponding author
Acknowledgments: Benedict Dellaert and Bas Donkers thank Netspar for financial support for
part of their research. The participation of Joffre Swait in this workshop was supported by the
Australian Research Council Discovery Project DP140103966. Caspar Chorus would like to
acknowledge support from the Netherlands Organization for Scientific Research (NWO Vidiproject 016-125-305).
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INDIVIDUALS’ DECISIONS IN THE PRESENCE OF MULTIPLE GOALS
Contribution statement
The aim of this paper is to provide directions on how individuals’ use of multiple goals can be
incorporated in econometric models of individual decision making. Integrating goals in
econometric models of choice allows for testing theories about goals on observed choice
behavior and can lead to an improved understanding and prediction of individuals’ choices. To
achieve this objective, the paper combines findings from research on individuals’ simultaneous
pursuit of multiple goals with current advances in choice modelling.
Abstract
This paper develops new directions on how individuals’ use of multiple goals can be
incorporated in econometric models of individual decision making. We start by outlining key
components of multiple, simultaneous goal pursuit and multi-stage choice. Since different goals
are often only partially compatible, such a multiple goal-based approach implies balancing goals,
leading to a deliberate goal-level choice strategy on the part of the decision maker. Accordingly,
we introduce a conceptual framework to classify different aspects of individuals’ decisions in the
presence of multiple goals. Based on this framework we propose a formalization of individual
decision making when pursuing multiple goals. We briefly review different previous streams on
goal-based decision making and how the proposed goal-driven conceptual framework relates to
earlier research in discrete choice models. The framework is illustrated using examples from
different domains, in particular marketing, environmental economics, transportation and
sociology. Finally, we discuss identification and modeling needs for goal-based choice strategies
and opportunities for further research.
Key words
Decision making, Goal-based decisions, Multi-stage decisions, Choice Modeling, Goal conflict
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Introduction
Balancing multiple goals in life is a challenge that most of us are all too familiar with. On
any given day we strive variously to improve our health, develop our careers, connect more
closely to loved ones at home, protect the environment, enjoy tasty food, and still find rest and
peace within ourselves to reflect on who we are. This is no small task, and depending on external
circumstances and the cognitive strategies that we employ, our achievement of these different
goals likely varies over time. An urgent deadline at work will force us to prioritize career-related
goals, while imposing a strict self-control regime will allow us to exercise every day to support
our health-related goals. The different goals we select and how we prioritize them affects our
behavior and the choices that we make (Austin and Vancouver 1996; Ding, 2007; Fishbach and
Ferguson 2007; Kopetz et al 2012). Yet only recently has research begun to address the question
of how goals can be directly incorporated in econometric models of individual decision making
to test theories about goals and improve our understanding and prediction of individuals’ choices
(e.g., Swait and Marley 2013, Marley and Swait 2017).1
We illustrate the challenge of combining goal-based theory and modeling
individuals’ choices of alternatives through an example. Assume a person is choosing a dessert
to buy for a dinner at home, and uses two personal goals in the selection: avoid gaining weight
and indulge in something tasty. The person might decide to set a threshold on each goal, and
only include desserts that exceed both thresholds (e.g., a tasty and healthy tropical fruit dessert),
or the person may choose - for the one special evening only - to focus exclusively on the goal of
tastiness (e.g., a rich chocolate brownie mousse). Note that at the goal activation level, the person
need not yet assign a unitary value to any dessert alternative, but decides on the goal strategy
level first; that is, from the perspective of goal pursuit, a strategy can be defined without
consideration of specific means (or options) of achieving them. Now change the framework from
attainment of the two goals to the more traditional choice modeling view of all possible desserts
having a utility value, which is a function of each dessert’s attributes. Then the model for the
selection would be to choose the dessert with the highest overall utility value (i.e., comparing
tropical fruit to brownie mousse) without taking into account the higher level goal trade-offs. By
ignoring the goal-level decision strategy, researchers, managers and policy makers would
1
At this stage we deal with the term “goal” in an informal and colloquial fashion. We provide a more rigorous
definition of the construct in the next section.
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develop an inaccurate understanding of how the individual’s decision is made, what might
change the individual’s decision (in terms of context, communications and alternative designs),
and hence how healthier outcomes for the individual could best be promoted.
The aim of this paper is to further developments in this direction by integrating research
on individuals’ simultaneous use of multiple goals with current advances in choice modelling.
The theoretical embedding of goal-based strategies in models of choice is important for a number
of reasons2. First, choice models that account for individuals’ goals may lead to better
predictions of consumer behavior. If different goals are (de)activated by individuals, they may
employ different decision strategies, different environmental cues may be important to them,
they may search different information sources, and ultimately they may select different
alternatives. Thus, behavioral differences between individuals, or within an individual at
different points in time or contexts, may depend on the goal-based strategies that they follow.
Second, these models may lead to new insights and explanations of behavior that cannot be
accomplished by simpler models (of either goal activation or choice alone). For example, goals
are also used by individuals to determine the allocation of scarce (cognitive) decision making
resources they have to arrive at better choices (Weber and Johnson 2009). In particular, the
difficulty of making a decision due to personal restrictions (say, cognitive or budgetary limits),
exogenous constraints (e.g., time and monetary limits) and contextual complexities (e.g., large
numbers of alternatives or many aspects), can be addressed through the introduction of multilayered decision strategies aligned with goal hierarchies and/or goal priorities. Third, the new
models may lead to new recommendations to managers or policy makers that cannot be
uncovered by a simpler model. In particular, by better understanding differences in goal
activation, managers and policy makers who wish to support individuals in making better
decisions (e.g., by living healthier lives, planning their finances better, or selecting between
education options) may be able to develop more effective policies and provide better assistance.
When individuals look for guidance in achieving better choice outcomes, a purely predictive
model at the level of their current alternative choices is of limited use in terms of guiding and
improving decisions. In contrast, a goal-oriented model is needed to also understand what
2
We thank an anonymous reviewer for constructive suggestions that helped us in more clearly highlighting these
three points.
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individuals are hoping to achieve and what goal-based decision strategy they would like to
employ to make their decisions.
Thus, based on workshop deliberations, we believe that the explicit extension of
econometric choice models to include simultaneous multiple goals will result in three types of
benefits compared to extant microeconomic and psychology theories: 1) consumer decision
outcomes will be more accurately predicted; 2) enhanced understanding about decision
processes; and 3) improved insights about consumers, hence novel policy recommendations.
While at this early stage goal-based choice models are in their infancy, and these claims are yet
to be firmly established, early empirical work is encouraging that the proposed goal-based
extensions to choice models will deliver on them.
Turning our attention to the remainder of the paper, we next outline the key components
of multiple, simultaneous goal pursuit and multi-stage choice. Since different goals are often
only partially compatible, such a multiple goal-based approach implies balancing goals, leading
to a deliberate goal-level choice strategy on the part of the decision maker. Therefore, we
introduce a conceptual framework to begin to classify different aspects of individuals’ decisions
in the presence of multiple goals, with an eye towards tying contextual and other covariates into
predicting goal (de)activation as an antecedent to eventual choice. This framework allows us to
also propose a useful formalization of individual decision making when pursuing multiple goals.
Next, we briefly review different previous streams on goal-based decision making. In addition,
we review how the proposed goal-driven conceptual framework relates to earlier research in
discrete choice models. We further illustrate the framework using examples from different
domains, such as marketing, environmental economics, transportation and sociology. Finally, we
discuss identification and modeling needs that arise from the question of whether incorporating
goal-level choice strategies can indeed improve the insights and predictive performance of
models of individual choice.
Setting the Groundwork for a Goal-Driven Choice Model
In this section we present and describe the goal-based model of choice that the workshop
participants evolved towards during the course of the Choice Symposium. We do this up front
because this model frames the remainder of the paper and clarifies that we are ultimately
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working towards the formulation of choice models that are not simply goal-sensitive, but are
fundamentally goal-driven3.
The psychology literature on goals is vast and of long-standing, as evidenced by the now
classic review of Austin and Vancouver (1996); it has only increased since then (see, e.g., Weber
and Johnson 2009). The former authors state: “… the sheer magnitude of this body of research is
associated with a certain danger. Heterogeneous perspectives can generate a large body of facts,
an excess of vocabulary, and numerous micro theories …” (Austin and Vancouver 1996, p. 338).
Thus, before undertaking the task of conceptualizing a choice framework based on goal pursuit,
it behooves us to spend some effort defining what we mean by “goals.” It seems most useful to
start by recognizing that many types of constructs of interest to psychologists and social
scientists might be expressed as, or reflected in, expressions of goals. For example, needs (e.g.,
hunger, thirst, entertainment) or values (e.g., statements about high-level abstract personal
characteristics that we would like to possess) can be expressed as goals, by including the
construct in a statement embodying pursuit of a desired end-state. Fundamentally, goals are
directive in nature, in that their pursuit will lead to desirable end-states. More effectively,
however, a taxonomy that recognizes that there exist functional goals whose attainment is
directly linked to product or service characteristics seems most useful for our purposes. More
abstract (i.e., non-functional) goals reflect the existence of a goal hierarchy that will require a
cascading of sub-goals that ultimately leads to a set of functional goals as they operationalize the
abstraction into ever more specific pursuits. Another distinction with a practical meaning for our
purposes is that goals can be approach or avoidance goals (Austin and Vancouver 1996, Table
2), indicating that their “valence” has a behavioral impact. Finally, we wish to point out that
decision makers can pursue functional goals (those they’re trying to accomplish through the
decision being made) as well as process goals (those they’re attempting to pursue that have to do
with how the decision is being made). In any particular modeling context, the focal goals may
come from multiple categories depending upon the problem features that are of greatest research
and substantive interest.
We begin by introducing the notion that a goal-driven choice requires operating in two
separate but related spaces (see Figure 1): 1) attribute space (with dimensions x1, x2, …), where
3
Van Osselaer and Janiszewski (2012) present a framework for goal-based choice that is motivated by ideas and
concerns similar to our own.
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alternatives (z1, z2,…) exist, and 2) goal space (G1, G2, …), with dimensions determined by the
decision maker. Note that the standard micro-economics description of decision making restricts
its attention to attribute space and overlays a value (or utility) function defined on attributes; the
highest value option is (usually) presumed selected. This depiction of the choice process also
comports the concept of attribute-based constraints, which leads to the creation of a choice set
ΥX of feasible alternatives (the “X” subscript refers to the attribute space, as defined above). The
generalization of the choice process from the point of view of goals is depicted in Figure 1.
Specifically, choice arises from a goal set ΓA ⊆ Γ (“A” for activated, and with Γ a (currently
vaguely defined) notional super-set of goals). The activation of goals results in a definition of the
metrics of success for the choice problem at the goal level.
In Figure 1, goals G1, G3 and G4 are activated by the decision maker, whereas G2 is not
(for example, this could be due to a lack of relevance to the decision or inability to attain it with
the options presented/available for choice). The double-headed block arrows between the
attribute space and ΓA are there for the purpose of reminding us that the (de)activation of goals
will be influenced by the attribute space via the alternatives, which in turn may be influenced by
the imposition of attribute-driven constraints reflecting one or more goals (e.g., a price limit).
--- Figure 1 about here ---
The core of the proposed framework is depicted in the goal-space diagram. We highlight
three important points about this framework. First, and perhaps foremost, the activation and
pursuit of multiple goals is directly represented in this space, without appeal to some kind of
value function. Second, decision makers can consider goals met or not for different reasons: one
goal may require that a threshold level be met, whereas another may need to be pursued to best
end (i.e., optimized). More generally, goals can be constraints and/or objectives. For example,
the figure makes the point that goal G1 ≥ τ1, which leads to the further elimination of alternatives
z4 and z5 (to remind, z2 was eliminated due to constraint H1 in attribute space), i.e., the choice set
of feasible alternatives in attribute-space, ΥX, can be further refined in goal-space to set ΥG. This
need not happen sequentially, and each of these sets can have unique elements. Third, decision
makers’ choice of establishing the role of goals, singly and in interaction, is what we suggest be
termed the goal choice strategy. That is, to make a product choice the decision maker must make
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two fundamental determinations with respect to goal space: 1) What are its dimensions (e.g.,
activate G1, G3, G4, but not G2) and 2) What constitutes the attainment of each goal (e.g.,
maximize G1, set a target on G3 and keep G4 above a threshold). These decisions are of prime
importance because they reflect the presumption that the decision maker’s choice is ultimately
determined in goal-space, not attribute-space, as commonly represented.
The mechanics of determining the level of attainment of Gk(z) by an alternative z is what
we term the goal evaluation strategy. The goal evaluation strategy may be a function of some or
all of the attributes that affect the achievement of alternatives on that goal, and the goal-specific
strategy may differ across goals. For example, the tastiness of a dessert may be determined by a
combination of its sugar and fat contents, its flavoring, etc.; its healthiness, for a gluten intolerant
individual, may be determined simply by whether or not it contains wheat.
Thus, the goal evaluation strategy does not affect the choice directly, but determines how
well alternatives perform on each of the different goals. The goal choice strategy then drives the
choice by combining the impact of the different goal evaluations on choice. A noteworthy point
to make here is that the goal choice strategy is not necessarily a function of anything involving
the lower-level outcomes (not the options, or their attributes, or the levels of those attributes, or
how one uses those to create a single evaluative scale, such as “utility”). That is, if one decides to
have a healthy dessert, one doesn’t necessarily first ask about which healthy desserts are
available.
Note that in this formulation we have employed approach goals or goals that decision
makers select to achieve or satisfy. The model could also be formulated with avoidance goals, as
mentioned above, with the corresponding change in algebra. However, one may also consider
behavioral aspects of approach versus avoidance goals. Heterogeneity may include individuals
who choose to achieve certain outcomes (healthy body weights), or avoid outcomes (obesity), or
who choose to achieve personal safety or avoid risky situations. The choice process outcomes
may differ for those who formulate these goals in the approach mode or avoidance mode,
perhaps somewhat akin to the outcomes arising from framing choices as gains or losses.
To illustrate these concepts, suppose a decision maker might decide on the strategy of
compensatory pursuit of goals G3 and G4 (refer to Figure 1), using weights that sum to unity,
while requiring that G1 ≥ τ1 and H1 ≥ d, where τ1 and d are two known thresholds. The goal
choice strategy for two goals G3 and G4 then is:
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Choose i∈M such that
max ΨM =
i∈M
∑ δ (w G ( X ) + w G ( X ) )
3
i
3
i
4
4
i
i∈M
s.t.
0 ≤ δ i ≤ 1 ∀i ∈ ΥX I ΥG
δ i = 0 ∀i ∉ ΥX I ΥG
∑δ
i
(1)
=1
i∈M
ΥX = {i ∈ M | H1 ( X i ) ≥ d }
ΥG = {i ∈ M | G1 ( X i ) ≥ τ 1}
In the goal-based decision problem (1), the objective function is specified as compensatory; it
could just as easily be defined as some non-compensatory function. For example, if the strategy
were changed to choose the option that disjunctively optimizes either G3 or G4, we simply
change the objective function in program (1) to
max ΨM =
i∈M
∑δ
i
max(G3 ( X i ), G4 ( X i ) )
.
(2)
i∈M
In our desire to work towards workable operational choice models of goal-driven
behaviors, formalisms of the type represented by (1) make it possible to envision model
specifications that directly reflect goal constructs. As we see above, mathematical program (1) is
capable of a) incorporating goals both as objectives (ΨM) and constraints (YG), b) handling
multiple goal pursuit (exemplified here with goals G3 and G4), and c) including attribute-space
constraints (YX). A decision may also be subject to what one might term “side” constraints, which
are neither attribute- nor goal-spaced based, such as a budget constraint. This extension requires
the introduction of constraints to (1), such as
∑δ p
i
i
≤I,
(3)
i∈M
where pi is the (exogenous) price of good i and I is the decision maker’s income.
Figure 1 shows one representation that extends our basic goal-driven decision framework
to handle such constraints: if the preferred option in goal-space meets these requirements, the
choice made in goal-space is final; if not, it is necessary to revise goals (e.g., activate or
deactivate some goal, change the relative importance of one goal relative to others), change goal
choice strategy or goal evaluation strategy, introduce or relax constraints in either attribute- or
goal-space, etc.
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An important point about Figure 1 merits repetition: this framework is meant to imply
that choice is determined in goal-space, not attribute-space. The framing of the decision problem
is made in terms of the goals and how they are to be used to make a choice – i.e., determination
of the goal choice strategy; secondarily, how individual goals are “calculated” is established –
i.e., determination of the goal evaluation strategy. Attribute-space contributes essential
information about constraints and other relevant information, but it is not where choice is
determined.
A Brief Review of Goal Based Decision Models
Empirical models
There is burgeoning empirical and theoretical research on goal-based choice at the
intersection of Cognition and Brain Sciences, Neuroscience, and Neuroeconomics, both for
individuals making decisions in isolation and in social situations (Glimcher and Fehr 2014).
A recent example of research into functional goals is Larrick (2016) who presents a framework
for decisions in social contexts similar to that considered in the present article. He introduces
“Objectives to achieve with the decision” (whereas we conceptualize goals as desired levels of
attainment of objectives), and presents attributes as “indicators/means of achieving objectives.”
Another example is Orehek and Forest (2016) who include relationship partners as means to
goals, and suggest that satisfying relationships are achieved when partners experience mutually
perceived instrumentality - that is, each partner feels instrumental to his or her partner’s
important goals and simultaneously perceives the partner as instrumental to their own important
goals. This perspective guides their predictions about how people cope with relationship loss
(e.g., due to death of a partner) - individuals with social networks that can substitute for a lost
partner should find partner loss less devastating. Other related recent work by Etkin (2016)
studies variety goals in committed relationships; for example, when planning a weekend
together, one member of a couple could choose more varied activities (e.g., going out to dinner,
to a movie, and to a concert) or less varied activities (e.g., going to three different movies).
Etkin’s experimental work shows that when consumers perceive more (respectively less) future
time ahead in a (committed) relationship, they prefer more (respectively less) variety in joint
consumption; the studies also reveal different choices for solo versus joint consumption. As the
author notes, these results have implications for recommendation systems. For example, suppose
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a couples’ most recent vacation was a Caribbean cruise; then a good recommendation for a
younger relationship would be a very different trip (e.g., a wine tour), and, for an older
relationship, a similar trip (a cruise to a different place). This prediction seems to run counter to
popular expressions such as “you only live once” and the notion of a “bucket list” and suggests
the need for further studies that control for how much variety a couple, and the individuals in the
couple, have achieved to date.
A person’s values are the principles or standards of their behavior, and involve their
judgment of what is important in life; thus, they might be considered to be determinants of their
objectives and/or goals. Kasser (2016, pp. 490-491) assumes that the value of materialism
“reflects the extent to which an individual believes that it is important to acquire money and
possessions, as well as to strive for the related aims of an appealing image and high
status/popularity, both of which are frequently expressed via money and possessions”; he
presents research on participants’ ratings of the importance of a variety of goals, with
materialism assessed by calculating the importance of extrinsic goals for financial success (“I
will be financially successful”), image (“My image will be one others find appealing”) and
popularity (“I will be admired by many people”) relative to other goals.
When we look at process goals, a habit leads a person to repeat the same behavior in a
recurring context. Adamowicz and Swait (2013) characterize habits as decision strategies that
minimize cognitive effort and Wood and Runger (2016) characterize them in terms of their
cognitive, motivational, and neurobiological properties; the latter review summarizes
computational models of habit and deliberate goal pursuit, with habit the learned efficient default
mode of response. The main classes of models presented are based on 1) artificial neural
networks with two interlinked habit and goal networks (Cooper et al. 2014); 2) a cognitive
architecture within the adaptive control of thought-rational (ACT-R) framework where
behavioral control shifts from an internal, declarative task representation (goal-based) to one
based on environment cues (habit-based) (Taatgen et al. 2008); 3) cognitive neuroscience models
of reinforcement learning (RL) where goal-directed learning involves mental simulation and
planning (model-based learning ) and habit formation involves trial-and-error learning to
estimate and store long-run values that are available in different states or contexts (model-free
learning) (FitzGerald et al. 2014).
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Cognitive neuroscience RL models are driven by, and in turn drive, neuroscientific
research using functional neuroimaging with human subjects, with the latter providing evidence
on neural systems that integrate habit and goal directed action (see Wood and Runger’s section
Neurobiology of Habits). O’Reilly et al. (2014) present a computational framework in which
goal-driven behavior is in place from the start, not arising solely in reaction to the environment
as it is typically conceived in RL models. Their main hypothesis is that there are two
qualitatively different states of mental life: goal selection where a decision maker carefully
considers the cost and benefits of possible goals, and goal engaged where a decision maker has
decided to try to achieve one or more goals. They assume the neural system is motivated to have
at least one goal engaged, and once a goal is engaged it dominates the evaluation of options and
resists disengagement. The framework is implemented in a biologically-constrained
computational algorithm, with the constraints motivated by known features of relevant brain
areas and mechanisms, and can potentially explain a wide range of clinical disorders. There is
extensive related work in Neuroeconomics, mainly studying brain-based representation and
learning of values; in particular, see Glimcher and Fehr (2014, especially Daw and Doherty’s
chapter) and the review by Konovalov and Krajbich (2016).
Normative models
The analysis of decision making has received equal attention from the perspective of
prescriptive or normative models. From the prescriptive perspective the aim is to develop models
that can support decision making, rather than to describe or predict it. In prescriptive approaches,
the notion of goals of the decision maker plays a central role, as without knowledge of the
decision maker’s goals it would be impossible to identify what the best decision is in any given
decision problem. Being able to generate a list of the objectives to be achieved by the decision of
concern is an axiomatic assumption of decision analysis that is part of the foundation of the field
as put forward by Von Neumann and Morgenstern (1947) and Savage (1954). As Keeney (2009)
puts it “if the set of objectives has not been generated then there is no basis to even think about
which alternatives may be better than which others and why this is the case.”. Thus, where one
could possibly disregard goals in descriptive approaches, they constitute the corner stone of
prescriptive frameworks.
To discuss how the notion of goals has been incorporated in prescriptive decision models,
it is helpful to make a distinction between two phases in decision making processes, namely 1)
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structuring the decision problem, and 2) evaluating the decision alternatives for making a choice.
Decision support methods generally intend to support both phases. Knowing one’s goals or
objectives is essential to structuring a decision problem, as goals provide the basis for both
identifying decision alternatives (creating a choice set) and specifying preference functions for
evaluating the alternatives. Failure to identify underlying objectives is a well-known pitfall in
decision making (Bond et al. 2010). Developing a goal-hierarchy is an example of a method that
is advocated to avoid overlooking essential evaluation criteria. In this method, the user creates a
goal tree by iteratively deriving sub-goals from main goals (of the organization) until they are
operationalized as criteria on which alternatives can be evaluated (Saaty 2008).
The adoption of goal-based approaches in the choice phase is less evident in multicriteria decision analysis. A variety of approaches have been developed for supporting evaluation
and making a decision. Different standpoints are taken in approaches with respect to the question
whether value can be represented through a closed-form preference function. MAUT (MultiAttribute Utility Theory) and AHP (Analytical Hierarchy) are two mainstream approaches where
value is expressed through a linear additive (closed-form) function of attributes/criteria. MAUT
models assume a utility function similar to the linear-in-parameters utility functions assumed in
(descriptive) discrete choice models. AHP also uses the idea of a closed-form evaluation
function. However, the approach to estimating relative values of criteria as well as scores of
alternatives differs (i.e., a geometric mean or eigenvalue method is used). On the other hand,
outranking methods, a third important category of multi-criteria decision analysis methods, do
not assume a closed form evaluation function. Outranking methods emphasize that a complete
ranking of alternatives on the given decision criteria may not be feasible. Their purpose is to
perform an initial screening and produce a short list that is presented to the decision maker for a
thorough trade-off (Olson et al. 1999).
The closed-form evaluation functions as used in MAUT, AHP and other approaches
generally assume a compensatory structure. Non-compensatory decision rules, on the other hand,
have also received attention especially in case of decision problems where the number of
decision alternatives is large or the solution space is not even continuous. In goal programming,
the decision problem is structured by means of a goal achievement function where a target value
is defined for each goal of the decision at hand and the best solution is defined as the solution
that minimizes the total deviation from targets across the goals. Although many forms of non-
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compensatory decision rules are possible, the lexicographic rule and the minimax rule are the
most commonly used non-compensatory rules in goal programming (Romero 2004). In case of a
lexicographic rule no trade-offs are made between goals that are placed in different priority
levels, whereas if a minimax rule is used the maximum deviation across goals is minimized (a
solution is as good as the performance on the goal on which it performs poorest).
To conclude: what evidence do we find for goal thinking in prescriptive approaches to
decision making? As the above short review suggests, goal-thinking is evident in the decision
problem structuring phase: knowledge of decision making goals is considered essential for
defining preference or goal-attainment functions to evaluate alternatives. In MAUT, AHP and
other approaches that assume linear additive evaluation functions, goals are less central to the
approaches, but implicitly represented in functions that define performance of alternatives
typically in attribute space. Outranking methods are more conservative and assume that some
trade-offs may be too complex to be made in such a fashion and leave those to the decision
maker. Furthermore, in goal programming approaches, goal achievement functions assuming
more complex trade-offs in goal space are relatively standard.
The Proposed Goal-Driven Model of Choice Relative to Other Discrete Choice
Models
In this section we briefly review the relationship between our proposed conceptual
framework and traditional discrete choice models and in particular how the latter have been
expanded over the years to incorporate various behavioral effects. We focus on the systematic
component of the discrete choice models. While much research has also addressed random error
components in models of choice to capture heterogeneity in taste (e.g., random coefficient or
mixed logit models, see McFadden and Train 2000), or heteroscedasticity between decisions or
individuals (e.g., the generalized multinomial logit model, Fiebig et al. 2010), in this overview
we are mainly concerned with the structure of the systematic components in the model that
capture variations in decision processes or decision weights.
The traditional discrete choice model connects alternatives’ attributes and their levels to
the underlying latent utility that consumers derive from these levels (McFadden 1980 – see
Figure 2 here). Alternatives can differ in the attribute levels that they offer (e.g., consumer
products, transport modes, jobs, potential romantic partners). Individuals evaluate the
attractiveness of these levels and integrate these evaluations into an overall utility that they attach
15
to each alternative. Individuals’ choices depend on the overall utility per alternative and they
choose the alternative with the highest utility subject to their financial budget and their time
constraints.
Figure 2 Structure of traditional discrete choice models
Based on insights into behavioral effects in individual decision making, such as the use of
heuristics or evaluation biases, these traditional discrete choice models have been expanded to
also allow for the use of different decision rules. For example, flexible models have been
proposed to capture whether individuals use conjunctive or disjunctive decision rules (Gilbride
and Allenby 2004, Hauser et al 2010); shifts in decision rules based on the complexity of the
decision task (Swait and Adamowicz 2001; Dellaert et al. 2012); the use of cut-offs in decisions
(Swait 2001); and the impact of regret on decisions (Chorus et al. 2008).These models have in
common that they augment the traditional discrete choice model with an additional modelling
layer that captures variations in how different attribute levels are integrated in a heuristic
decision rule that results in an individuals’ choice of an alternative (see Figure 3).
16
Figure 3 Structure of behavioral discrete choice models that allow for decision heuristics
Recent work has taken a different angle on extending discrete choice models: instead of
focusing on decision heuristics, it has addressed the question what higher order benefits
consumers look for when making decisions. This research builds on early work in, e.g.,
marketing, which highlighted that consumer needs may vary across situations (e.g. Myers 1976)
and that the evaluation of attribute levels may depend on the degree to which they can help fulfill
underlying consumer needs (e.g., Gutman 1982; Dellaert et al. 2008; Ter Hofstede et al. 1999).
Researchers have proposed different models that can capture this intermediate layer in the utility
formation process. Hybrid choice models have been proposed that flexibly incorporate such
intermediate evaluation steps (Ben-Akiva et al. 2002). These evaluations have been treated as
latent (benefit) variables by some researchers (Kim et al. 2016; Paulssen et al. 2014), while other
researchers have explicitly elicited attribute-benefit connections from individuals (Arentze et al.
2015). Thus, in these models, the attributes of each alternative drive how the alternative is
evaluated on each of a set of different benefits that consumers look for and the utility that is
subsequently attached to the different benefits determines the consumer choice. Figure 4
summarizes the structure of this second stream of choice model extensions.
17
Figure 4 Structure of benefit-based discrete choice models
In this article we propose a different approach to the development and motivation of
discrete choice models that is conceptually related to benefit-based discrete choice models but is
also fundamentally different in that it explicitly acknowledges that individuals may also form
decision strategies at the level of the goals they pursue. More precisely we view a goal as
conceptually similar to an individual’s desire to achieve a certain benefit (e.g., an individual’s
goal of eating healthily aligns with looking for the benefit “healthy” in food; an individual’s goal
of saving effort aligns with looking for the benefit “convenience” in transportation; etc.).
However, we propose that achieving goals is not a neutral process that translates directly to
utility (such as in the benefit-based models), but that much as in the case of heuristics and
attribute-level evaluations, individuals may choose to follow a certain goal strategy to come to a
choice. For example, they may focus on one goal at the expense of all others, or they may
balance different goals when making a decision. Thus in evaluating alternatives, individuals
follow a two-step approach, for which the order of cognitive processing may vary. Step one is
the goal evaluation strategy in which alternatives are evaluated in terms of how well they
perform in achieving a certain goal (e.g., are Brussel sprouts healthy? are they tasty?). Step two
is the goal choice strategy, in which the individual determines how to integrate the fulfillment of
different goals into the selection of an alternative (e.g., focus exclusively on health outcomes of
18
food, or balance healthiness against tastiness). This proposed structure is graphically summarized
in Figure 5 and was formalized in the proposed conceptual model at the start of this paper.
Figure 5 Structure of goal-based discrete choice models
Applications in different fields
Marketing
As an example from marketing on the impact of goal choice strategies, consider the case
of a consumer’s choice of a new home. Multiple (often competing) goals are likely activated
when someone is choosing a new home, and activated goals will constrain the feasible attribute
space. For example, a desire to achieve or maintain financial security creates a home price
ceiling (i.e., rent, mortgage), while other competing goals such as achieving social status or
having well-educated and accomplished children translate into a price minimum threshold and
neighborhood location constraints (e.g., acceptable school districts). A work-family balance goal
may further constrain neighborhood location (i.e., commute time) as well as property size and
condition (i.e., required maintenance and repair time). Household budget constraints limit the
19
feasible attribute space and goal space, as illustrated in Figure 1, especially for low and middle
income households, and goal importance/activation is likely to be updated as the choice process
progresses. Heterogeneity in the relative importance, or activation, of each goal will manifest in
different decision strategies across a cross-section of consumers. Similarly, we expect to observe
heterogeneity within households for repeated home choices over time as the relative importance
of each goal evolves over the lifetime of the decision-maker.
Environmental Public Policy
Two examples of areas in environmental economics that employ models of choice and in
which goal choice strategies may be impactful are recreation demand models (intended to
capture the behavior associated with recreation site choice and measure values arising from
changes in environmental attributes), and models of passive use value (intended to capture
monetary values for changes in public goods or publically provided goods). In both of these
cases multiple goals may play a significant role in decision making. In the recreation context,
goals may include fitness, socializing, desire for solitude, developing skills (e.g., rock climbing
or birdwatching), passing on traditions to children (e.g. hunting or fishing skills) or expanding
experiences (e.g., life-lists for birdwatchers). These goals may compete with each other and may
differ across social contexts (family trips versus trips with colleagues). Morey and Thiene (2012)
examine such recreational choices in the context of “life constraints” but this approach can be reconsidered in a goal-based framework. In the elicitation of passive use values, which is typically
framed as a referendum between policy options, there is a long history of discussion of the
competing goals of being a “citizen” (altruistic, socially minded) versus a “consumer” (rational
self-interested individual). These different goals have been examined empirically (Blamey 1996)
and conceptual models have been developed to reflect these goals and the factors that affect the
balance between them. Levitt and List’s (2007a,b) model that outlines conflict between
moral/ethical goals and personal wealth goals has been used as a framework to understand
choices in passive use value (as well as other) contexts.
Transport
The role of goals in transport choice has been studied in the context of sustainable
transportation mode choice. Individuals who cherish feelings of power and pleasure (power and
hedonic goals) put a high relevance on owning their own transport mode, which positively
20
affects their choice of using their private car for trips (Paulssen et al. 2014). Individuals who are
interested in pleasure and sensuous gratification for oneself right now (hedonic and egoistic
goals) are more likely to use the automobile instead of adopting pro-environmental behaviors
(Steg et al. 2014).
Competing goals may also be activated in particular situations. For example, in choosing
the type of car to buy, a goal of behaving in an environmentally friendly manner which would
lead, say, to the choice of an electric vehicle can compete with a goal of achieving social status,
which might lead toward the choice of a luxury car. Since luxury electric vehicles are extremely
expensive, achieving both goals might be limited by the individual’s budget constraint.
Individuals’ final choices will depend then on their goal choice strategy and how each alternative
is evaluated on the different goals (i.e., goal evaluation strategy).
Sociology
As an example in the domain of sociology, multiple, potentially competing goals are
likely activated when searching for a romantic relationship. For example, a woman seeking a
marriage partner might restrict her search to men who are financially secure to increase her own
financial security as well (Oppenheimer 1988). But the pool of financially secure men tends to be
older than the general population; pursuit of this goal would increase her likelihood of ending up
with someone much older and previously married, which could conflict with the goal of having a
conventional (non-blended) family arrangement. Moreover, a woman in the dating market may
have multiple goals that span very different time horizons, constraining the attribute space. For
example, she might eventually wish to be married, but in the short term wants to have fun and
explore her options. In selecting a goal choice strategy, she must decide how much effort to
allocate to achieving these two goals, and this in turn may imply very different subsets of the
attribute space that she will consider. Different mate seekers may allocate different levels of
effort to these two goals, resulting in heterogeneous goal choice strategies.
It is interesting to note that, while goals constrain attribute space, attribute space may also
constrain the emphasis put on certain goals and hence the goal choice strategy. For example, the
local dating pool is a major constraint that limits feasible attribute space, as it determines who is
available to mate seekers. What goals are activated may also depend on some overall evaluation
of the set of available options—i.e., whether the total available set is deemed overall desirable or
undesirable (e.g., Luce, et al. 2000). When desirable options are thin on the ground, a mate
21
seeker may abandon the goal of finding a long-term partner and instead focus on pursuing more
short-term encounters. Thus, in the presence of multiple goals, if one goal constrains attribute
space too much, the decision-maker may shift her focus to goals where she is more likely to
succeed.
Specific Examples of Implementation
To focus the applications further, we present two recent examples of the application of
such steps toward goal-based research. They are Swait, Argo and Li (2017, based on Li (2013))
and Souza (2015). The former developed an econometric model for latent (functional) goal
identification using traditional stated preference (SP) data. A propos the issue of validation,
Swait et al. (2017) employ one Discrete Choice Experiment (DCE) concerning hypothetical
digital camera profiles to estimate model parameters, then use a second DCE with real digital
cameras on an independent sample from the same population to conduct out-of-sample forecasts.
They find that the out-of-sample forecasting of the goal-based model is markedly superior to a
standard latent preference class model. Similarly, Souza (2015) investigates the screening role of
(latent) goals in choice set formation. Following model estimation, he uses content analysis of
user-provided motivations for screening to validate econometric inferences. Both references
illustrate the usefulness and increased precision of interpretation arising from a goal-based
modeling approach, reinforcing that researchers should plan robust validation exercises as part of
model development and testing. A further application in the environmental economics area is
Thiene, Swait and Scarpa (2017) who examine the role of goals in choice set formation. They
include responses to motivation or goal questions as factors explaining the probability of
inclusion of alternative rock climbing sites in an independent availability model of choice set
formation.
Measurement and estimation
Whereas empirically-oriented social scientists in the past had to make do with scarce
databases or engage in costly primary data gathering, we live today in a Golden Age of relative
data abundance. Due to the ubiquity of internet-enabled search (e.g., Google) and procurement
(e.g., Amazon) – with matter-of-course recording of intermediate way stations and blind alleys –
22
the ability to rigorously model not only eventual choice but the process of goal attainment has
increased dramatically.
However, voluminous data isn’t synonymous with appropriate data, and many challenges
remain for data collection, measurement, and inference when one wishes to estimate goal-based
models of choice. The standard workhorse in the field is the discrete choice model, descending
from McFadden’s conditional logit and successively broadening to full-on Bayesian machinery
to capture hierarchical choice processes, systematically and incidentally missing data, and data
fusion (Feit et al. 2013). But such methods are not designed to automatically model the intricate,
time-bound processes of goal choice strategies that involve seeking, revising, and evaluating the
attainment of goals (or lack thereof). Here, we consider some extensions, in terms of data
requirements and model developments, towards that end specifically.
Let us consider again our examples in the domains of marketing, environmental public
policy, transport, and the sociology of mate choice. Although these examples were chosen to
highlight the vast range of goal choice strategies, they share certain features, some of them
differentially implicated in goal orientation. Perhaps chief among these is the notion of
constrained optimization: in the extant canonical choice theory setting, the decision-maker must
maximize outcome utility under a cost (time, money, cognition) constraint; but in a goal choice
setting, options highly attractive in one goal’s context may be ruled out by another. For example,
in housing search the “perfect home” may be struck for lacking a good neighborhood primary
school, although one does not yet have children; a vacation consistent with the goal of “lifetime
learning” may not score high on “fun with friends”; a family car that provides safety and suitable
accoutrements may interfere with savings for children’s college; or looking for an “established,
stable provider” might translate into their already having had a family with a prior partner.
In each of these cases, the choices made by the decision-maker can involve not only the
attributes and utility weights of others (Aribarg, et al. 2010), but the activation of, and conflict
among, multiple goals, some of which are not traditionally part of the decision domain. For
example, living in a part of the city that enables one to meet a wide variety of potential mates
may entail rental costs that preclude purchasing a car or a home of one’s own. As such, to
calibrate not just models of choice, but models of choice under multiple goal pursuit requires
both a re-examination of constraint-based optimization and the sorts of data that that would
require: how particular decisions not only impact goal attainment, but constrain seemingly
23
unrelated options down the road for additional goals. Specific information that could inform a
hierarchical setting would involve geo-demographics, lifestyle stage, financing and credit score
information, as well as “market baskets” for both non-durable and durable products. Ideally, such
information would be available for an entire co-habitating unit, to begin to unravel the multiple
goals and constraints implicated by seemingly “individual” decisions.
To this end, data from a wide variety of choices and intermediate stages of choice would
be especially useful: not only which widget was eventually purchased, but the full act of widget
information search, across providers. To obtain anything like causal statements – e.g., “she did
that because the goal of completing her education took precedence over purchasing a car” –
researchers would ideally make use not only of field data, but field experiments. For example, a
web site offering information on homes for sale might differentially prioritize information
pertinent to children (school quality, proximity to play areas, neighborhood composition, etc.) to
determine how goals related to that affect site usage and eventual home choice. If such
experiments are coordinated across providers – not only housing searches, but other information
or purchase-oriented sites – a unified model of goals could be formulated from this sort of crosscontext, individual-level data.
Even with such ideal data, the process of measurement is fraught with complexities. The
sheer number of variables and their interactions would require either highly theory-driven, “top
down” searches for patterns (e.g., “we sought three types of consistent reaction”) or what is
essentially the opposite: a “bottom up” machine-learning or nonparametric approach. So-called
Big Data would not only enable, but practically invite, the latter, where unsupervised learning
could pick out salient patterns across data types while sidestepping attrition, missing information,
and other distracting arcana of real data sets. Indeed, such a revolution has begun, with the
automated extraction of marketing “meaning” from large corpora (Tirunillai and Tellis 2014) and
the use of high-dimensional nonparametric models to account for complex nonlinear and nonmonotonic data relationships (Bruch, et al. 2016). The use of such methods is presently
constrained by storage, manipulation, and processing constraints, although recent advances in
both classical and Bayesian estimation (Hoffman and Gelman 2014), as well as the inexorable
upward trajectory in computational speed, will increasingly enable researchers to fashion flexible
models of goal attainment on available, large-scale data sources.
24
Model identification and a partial research agenda for testing the model
Combining Occam’s razor and the need to search for evidence in outcome data (choices)
brings us to the formulation of a key research question: When does a goal-based model of choice
behavior provide better predictions of choice behavior than other (and simpler) models that do
not feature a goal layer; and does the increase in predictive accuracy justify the loss in
tractability?
The latter part of this question is essentially subjective, but the part preceding is objective
in the sense that there are established procedures for determining the predictive performance of a
choice model. A goal-based choice model’s potential to generate better predictions than other
models crucially depends on its ability to make predictions that differ from those made by other
models (in the sense of assigning different choice probabilities to alternatives).
This also raises the question: what other models should one use as benchmark? Even
though there are many ways to penalize model complexity in statistical testing procedures,
comparing a full-fledged goal-based model with a linear-in-parameters multinomial logit (MNL)
model is not going to convince many scholars. For example, a choice model with a flexible error
structure and a non-linear specification of observed utility may be a more credible candidate for
goal-based models to be benchmarked against, rather than a simple MNL.
Combining these lines of argumentation, we suggest that the following research agenda
be followed to arrive at a test of the added value of the goal-based framework proposed in this
paper:
1. Find or collect data which in theory would allow the researcher to observe differences
between the predictions made by goal-based models and competing choice models.
2. Specify the appropriate goal-based model, as well as a range of other candidate models that
are likely to do well on the data; this class of competing models may include workhorse
models such as linear-in-parameter MNL models, but should also include several more
flexible and sophisticated models.
3. Estimate all models on the collected data, and perform extensive out-of-sample validation
tests. These validation tests can involve prediction of choices, but should also examine the
ability of competing models to predict goals that subjects explicitly articulate (but are not used
in estimation, of course). The objective of these tests should, at one level, be about
establishing predictive validity; but this does not preclude examining the external and
25
construct validity of models. Such tests are especially important when the goal-based model
consumes more degrees of freedom (parameters) than competitor models.
4. Carefully inspect the differences in predictions between different models, if necessary at the
level of individual choice situations/tasks. This may warrant the specific design of validation
data collection efforts, separate from model estimation data. Explore if the goal-based
model(s) differ from other models – in terms of predicted choice probabilities for particular
alternatives – in ways that align well with the behavioral notions on which the model is built.
If the choice modeler’s aim is simply to predict choice behavior, then the above
mentioned steps – preferably performed on a large number of data sets by different teams of
researchers – should suffice as a test of the potential of the goal-based approach. If one, in
addition, has the ambition to derive managerial or policy implications, then a next step is to
explore to what extent the goal-based approach may be translated into managerial or policy
actions that differ from those generated by competitor models. And ultimately the desire is to
construct a better understanding of the behavioral underpinnings of choice along with predictive
ability.
Discussion
Currently popular models of choice behavior are econometrically quite sophisticated
compared to the workhorse linear-in-parameters MNL models of the 1970s. Driven by scholars’
ambitions to explain ever more subtle (latent) behavioral phenomena and aided by phenomenal
advances in computational power, the choice modeler’s toolbox has over the years been enriched
with a wide variety of alternative model specifications. Initially, developments focused on
incorporating more sophisticated error term structures to more closely represent observed
substitution patterns; but more recently, doubtlessly inspired by the growing exploration of the
judgment and decision theory literature and the increased popularity of behavioral economics,
attention has shifted towards incorporating psychological insights in choice models.
In many instances, choice modelers with an interest in the behavioral sciences point at the
often compelling intuitions underlying ‘behaviorally realistic’ choice models. However, these
intuitions (e.g., that people use higher level goals when trading off attributes of choice
alternatives) usually refer to the process of decision making, while the data used to validate
26
models tends to consist of decision outcomes (i.e., choices). What’s more, process data, such as
verbal reports on mental processes or activated neurons in case of fMRI studies, have been
criticized for being unreliable.
This may require a Milton Friedman style ‘as if’ conceptualization: since we are unable
to rigorously verify the process assumptions underlying our model (i.e., the role of high-level
goals), we are forced to search for proof in observed choices. This challenge is not dissimilar to
the early discussion in economics of the movement from demand for “goods” versus demand for
attributes. Before Lancaster (1966) there wasn’t much discussion on the topic of attributes (at
least in economics). It took a significant amount of time, and a different approach to data
collection and analysis, before attribute based approaches were fully employed in the literature.
Another example of such shifts in approach is reference dependence: structural models
embodying the concept have only appeared fairly recently, even though reduced form models
that included reference dependence clearly worked better at predicting individual choice. Goals
may present difficulties because they are generally unobservable, but that is also the case with
reference dependence. The observer doesn’t really know what people deem the reference
condition to be, and it may not be the current state (the usual assumption made in analysis).
In Figure 1, the double-headed arrow between the goal activation block and the goalspace representation is meant to convey the idea that the inability to attain one or more goals at a
satisfactory level can lead to goal importance adaptation as well as (de)activation of other goals
(Li 2013, Swait et al. 2017). Another way to state this is that people adapt to the decision context
in part by changing their goal pursuit behavior if their desired goals are not attainable, perhaps to
the point of deactivating some goals and activating others.
Readers will have noticed there has been little mention of the income or budget constraint
(though see discussion following expression (2)). The traditional place to incorporate the income
constraint is in attribute-space, much in the form shown for constraint H1 (see Figure 1 again).
From a process perspective, however, it seems to us that an alternative representation is that the
budget and other exogenous constraints can be conceptualized following the selection of the
preferred alternative(s) in goal-space by checking for the budgetary impact of the choice(s)
determined through goal pursuit. If the budget constraint is met, the decision maker can move to
implementation; however, if the budget constraint is violated by the desired outcome, it is
necessary to return to earlier stages and implement constraints in attribute-space or revise goal
27
activations and importances so as to determine a final, feasible choice. An alternative depiction is
that such constraints may be at work in the goal (de)activation stage (reduction of Γ to ΓA),
dictating which goals should be pursued and what importance should be assigned to them.
Interesting possibilities arise from this second conceptualization: e.g., if someone projects that a
budget constraint cannot be met for one goal if another is achieved (I may not be able to afford a
car if I take a year off to travel to learn about shamanic traditions along the Silk Road; or I may
not be able to support my family if I pursue my dream of being an artist), they may not activate
certain goals. And as mentioned above budget information may enter in the construction of
process based goals. The important issues here center on the stochasticity and future-orientation
of the activated goals under the influence of budget constraints.
Conclusion
In this paper we have outlined key components of multiple, simultaneous goal pursuit and multistage choice, and have proposed how individuals’ use of multiple goals can be incorporated in
econometric models of individual decision making. Since different goals are often only partially
compatible, a multiple goal-based approach implies balancing goal leading to a deliberate goallevel choice strategy on the part of the decision maker. Therefore, we introduced a conceptual
framework that classified different aspects of individuals’ decisions in the presence of multiple
goals. (Figure 1). At the core of the proposed framework is the distinction between decision
makers’ goal choice strategy (i.e., the individual’s choice of establishing the role of goals, singly
and in interaction; see Marley and Swait 2017) and their goal evaluation strategy (i.e., a function
of some or all of the attributes that affect the achievement of alternatives on a goal). The goal
choice strategy is of prime importance in the proposed framework because it reflects the fact that
the decision maker’s choice may ultimately be determined in goal-space, not attribute-space, as
is more commonly represented. In that case, the goal evaluation strategy does not affect the
choice directly, but determines how well alternatives perform on each of the different goals.
Thus, by discussing the main components as well as identification and modeling needs for the
proposed goal-based choice strategies we hope the current paper can stimulate further empirical
research on the theoretically promising as well as practically relevant topic of individuals’
choices in the presence of multiple goals.
28
References
Adamowicz, W. L., and Swait, J. D. (2013). Are food choices really habitual? Integrating habits,
variety-seeking, and compensatory choice in a utility- maximizing framework. American
Journal of Agricultural Economics, 95: 17- 41.
Arentze, T. A., Dellaert, B. G., and Chorus, C. G. (2015). Incorporating mental representations in
discrete choice models of travel behavior: modeling approach and empirical application.
Transportation Science, 49(3), 577-590
Aribarg, A., Arora, N. and Kang, M.Y., (2010). Predicting joint choice using individual data.
Marketing Science 29(1), 139-157.
Austin, J. T., and Vancouver, J.B. (1996). Goal constructs in Psychology: Structure, process, and
content. Psychological Bulletin, 120(3), 338-375.
Blamey, R.K. 1996. “Citizens, Consumers and Contingent Valuation: Clarification and the
Expression of Citizen Values and Issue-Opinions. In Forestry, Economics and the
Environment, ed. W. L. Adamowicz, P. Boxall, M. K. Luckert, W. E. Phillips and W. A.
White, 103–33. Wallingford: CAB International.
Ben-Akiva, M., McFadden, D., Train, K., Walker, J., Bhat, C., Bierlaire, M., Bolduc, D.,
Boersch-Supan, A., Brownstone, D., Bunch, D.S., De Palma, A., Gopinath, D., Karlstrom, A.
and Munizaga, M.A., (2002). Hybrid choice models: progress and challenges. Marketing
Letters, 13(3), 163-175.
Bond, S.D., Carlson, K.A. and Keeney, R.L. (2010). Improving the generation of decision
objectives. Decision Analysis, 7, 238-255.
Bruch, E., Feinberg, F. and Lee, K.Y. (2016).Extracting multistage screening rules from online
dating activity data. Proceedings of the National Academy of Sciences, 201522494.
Chorus, C. G., Arentze, T. A., and Timmermans, H. J. (2008). A random regret-minimization
model of travel choice. Transportation Research Part B: Methodological, 42(1), 1-18.
Chorus, C. G., and Kroesen, M. (2014). On the (im-) possibility of deriving transport policy
implications from hybrid choice models. Transport Policy, 36, 217-222.
Cooper, R. P., Ruh, N., Mareschal. D. (2014). The goal circuit model: a hierarchical multi-route
model of the acquisition and control of routine sequential action in humans. Cognitive
Science, 38:244-74.
Daw, N. D., and O’Doherty, J. P. (2016). Multiple systems for value learning. In P. W. Glimcher
and E. Fehr (Eds.). Neuroeconomics: Decision Making and the Brain. 2nd Edition. Chap.2 1,
p.393-410 Elsevier.
Dellaert, B. G.C., Arentze, T. A., and Timmermans, H. J. (2008). Shopping context and
consumers’ mental representation of complex shopping trip decision problems. Journal of
Retailing, 84(2), 219-232.
Dellaert, B. G.C., Donkers, B., and Soest, A. V. (2012). Complexity effects in choice
experiment-based models. Journal of Marketing Research, 49(3), 424-434.
Ding, M. (2007). A theory of intraperson games. Journal of Marketing, 71(2), 1-11.
Dzyabura, D. (2013). The role of changing utility in product search. Available at SSRN:
http://ssrn .com/abstract=2202904.
Etkin, J. (2016). Choosing variety for joint consumption in committed relationships. Journal of
Marketing Research, forthcoming.
29
Feit, E. M., Wang, P., Bradlow, E. T., & Fader, P. S. (2013). Fusing aggregate and disaggregate
data with an application to multiplatform media consumption. Journal of Marketing Research
50(3), 348-364.
Fiebig, D. G., Keane, M. P., Louviere, J., and Wasi, N. (2010). The generalized multinomial
logit model: accounting for scale and coefficient heterogeneity. Marketing Science, 29(3),
393-421.
Fishbach, A., and M.F. Ferguson (2007). The goal construct in social psychology. In A. W.
Kruglanski and T. E. Higgins (Eds.), Social Psychology: Handbook of Basic Principles (pp.
490-515). NY: Guilford.
FitzGerald, T. H. B., Dolan R. J. , Friston K. J. (2014). Model averaging, optimal inference, and
habit formation. Frontiers in Human Neuroscience, 8:457.
Gilbride, T. J., and Allenby, G. M. (2004). A choice model with conjunctive, disjunctive, and
compensatory screening rules. Marketing Science, 23(3), 391-406.
Glimcher, P. W., and Fehr, E. (2014). Neuroeconomics: Decision Making and the Brain. 2nd
Edition. Elsevier.
Gutman, J. (1982). A means-end chain model based on consumer categorization processes. The
Journal of Marketing, 46(2), 60-72.
Hauser, J. R., Toubia, O., Evgeniou, T., Befurt, R., and Dzyabura, D. (2010). Disjunctions of
conjunctions, cognitive simplicity, and consideration sets. Journal of Marketing Research,
47(3), 485-496.
Hoffman, M. D., and Gelman, A. (2014). The No-U-turn sampler: adaptively setting path lengths
in Hamiltonian Monte Carlo. Journal of Machine Learning Research 15(1), 1593-1623.
Huber, Joel, and Noreen M. Klein (1991). Adapting cutoffs to the choice environment: the
effects of attribute correlation and reliability. Journal of Consumer Research, 18(3), 346-357.
Kasser, T. (2016). Materialistic values and goals. Annual Review of Psychology, 67: 489-514.
Keeney, R.L. (2009). The foundations of collaborative group decisions. International Journal
Collaborative Engineering, 1, 1(1-2), pp.4-18.
Kim, D.S., Bailey, R.A., Hardt, N. and Allenby, G.M. (2016). Benefit-Based Conjoint Analysis.
Marketing Science, forthcoming.
Konovalov, A., and Krajbich, I. (2016). Over a decade of Neuroeconomics: What have we
learned? Organizational Research Methods. doi: 10.1177/1094428116644502
Kopetz, C. E., Kruglanski, A.W., Arens, Z. G., Etkin, J., and Johnson, H. M. (2012). The
dynamics of consumer behavior: A goal systemic perspective. Journal of Consumer
Psychology, 22(2), 208-223.
Lancaster, K. J. (1966). A new approach to consumer theory. The Journal of Political Economy,
74 (2), 132-157.
Larrick, R. P. (2016). The social context of decisions. The Annual Review of Organizational
Psychology and Organizational Behavior. doi: 10.1146/annurevorgpsych-041015-062445
Levitt, S. and List, J. A. (2007a). Viewpoint: On the Generalizability of Lab Behaviour to the
Field. Canadian Journal of Economics 40(2), 347-370.
Levitt, S. and List, J. A. (2007b). What do Laboratory Experiments Measuring Social
Preferences Reveal about the Real World. Journal of Economic Perspectives 21(2), 153-174.
Li, L. (2013) The Role of Antecedent Volition on Consumer Evaluative Processes and Choice
Behavior, Doctoral Dissertation, School of Business, University of Alberta, Fall 2013.
Luce, M.F., Payne, J.W. and Bettman, J.R. (2000). Coping with unfavorable attribute values in
choice, Organizational behavior and human decision processes, 81(2), 274-99.
30
Marley, A.A.J., and Swait, J. (2017). Goal-Based Models for Discrete Choice Analysis,
Transportation Research Part B, , 101, 72-88..
McFadden, D. (1980). Econometric models for probabilistic choice among products. Journal of
Business, S13-S29.
McFadden, D., and Train, K. (2000). Mixed MNL models for discrete response. Journal of
applied Econometrics, 15, 447-470.
Morey, E., and Thiene, M. (2012). A parsimonious, stacked latent-class methodology for
predicting behavioral heterogeneity in terms of life-constraint heterogeneity. Ecological
Economics, 74, 130-144.
Myers, J. H. (1976). Benefit structure analysis: A new tool for product planning. Journal of
Marketing, 40(4), 23-32.
O’Reilly, R. C., Hazy, T. E., Mollick, J., Mackie, P., and Herd, S. (2014). Goal- driven cognition
in the brain: A computational framework. Manuscript, Department of Psychology and
Neuroscience, University of Colorado Boulder.
Olson, D.L., Mechitov, A.I. and Moshkovich, H. (1999). Comparison of MCDA Paradigms. In
N. Meskens and M. Rouben (eds.) Advances in Decision Modelling, pp. 105-119.
Oppenheimer, V.K., (1988). A theory of marriage timing, American Journal of Sociology, 94,
pp. 563-91
Orehek, E., and Forest, A. L. (2016). When people serve as means to goals: Implications of a
motivational account of close relationships. Current Directions in Psychological Science,
25:79-84. doi: 10.1177/0963721415623536
Paulssen M., Temme D., Vij. A. and Walker. J. (2014). Values, attitudes and travel behavior: a
hierarchical latent variable mixed logit model of travel mode choice. Transportation 41(4),
873-888.
Prato, G.G., Bekhor, and S., Pronello, C. (2012). Latent variables and route choice
behavior.Transportation 39 (2), 299–319.
Romero, C. (2004) A general structure of achievement function for a goal programming model.
European Journal of Operational Research, 153, 675-686.
Saaty, T.L. (2008) Decision making with the analytic hierarchy process. International Journal of
Services Sciences, 1, 83-98.
Savage, L. J. (1954). The Foundations of Statistics. John Wiley & Sons, New York, and
Chapman & Hall, 1954.
Souza, F. (2015) Goal-Based Choice Set Formation, Doctoral Dissertation, Institute for Choice,
Business School, University of South Australia, March, 146pp.
Steg, L., Perlaviciute, G., van der Werff, E. and Lurvink, J. (2014) The Significance of Hedonic
Values for Environmentally Relevant Attitudes, Preferences, and Actions. Environment and
Behavior, 46(2) 163- 192.
Swait, J. (2001). A non-compensatory choice model incorporating attribute cutoffs.
Transportation Research. Part B: methodological, 35(10), 903-928.
Swait, J., and Adamowicz, W. (2001). The influence of task complexity on consumer choice: a
latent class model of decision strategy switching. Journal of Consumer Research, 28(1), 135148.
Swait, J., Li, L., and Argo, J. (2017). Modeling Simultaneous Multiple Goal Pursuit and
Adaptation in Consumer Choice. Working Paper, Institute for Choice, University of South
Australia, Sydney.
31
Swait, J. and Marley, A.A.J. (2013). Probabilistic Choice (Models) as a Result of Balancing
Multiple Goals. Journal of Mathematical Psychology, 57, 1-14.
Taatgen, N. A., Huss, D., Dickison, D., and Anderson, J. R. (2008). The acquisition of robust and
flexible cognitive skills. Journal of Experimental Psychology: General. 137, 548-65.
Thiene, M., Swait, J., and Scarpa, R. (2017). Choice set formation for outdoor destinations: The
role of motivations and preference discrimination in site selection for the management of
public expenditures on protected areas. Journal of Environmental Economics and
Management, 81, 152-173.Ter Hofstede, F., Steenkamp, J. B. E., and Wedel, M. (1999).
International market segmentation based on consumer-product relations. Journal of
Marketing Research, 36(1), 1-17.
Tirunillai, S., and Tellis, G.J. (2014). Mining marketing meaning from online chatter: Strategic
brand analysis of big data using latent dirichlet allocation. Journal of Marketing Research
51(4), 463-479.
Van Osselaer, S.M.., and Janiszewski, C. (2012). A goal-based model of product evaluation and
choice. Journal of Consumer Research, 39(2), 260-292. Von Neumann, J. and Morgenstern,
O. (1947). Theory of games and economic behavior, 2nd rev. ed. Princeton, NJ, US:
Princeton University Press.
Weber, E.U., and Johnson, E.J. (2009). Mindful judgment and decision making. Annual Review
of Psychology, 60, 53.
Wood, W., and Runger, D. (2016). Psychology of habit. Annual Review of Psychology, 67: 289314. doi:10.1146/annurev-psych-122414-033417.
32
Figure 1 – Framework Relating Multiple Goal Pursuit to Choice
Γ
Attribute Space
Goal Space
ΓA
x2
G1
z4=(x41,x42)
z1
H1
ΥX
z3
G2
G3
z5
G3
z2
v(z1)={G1(z1),G3(z1),G4(z1)}
z6
ΥG
G4
x1
v(z6)
v(z5)
Goal Activation/
Deactivation
Constraint
revision
v(z4)
Goal
revision
G1
v(z3)
G4
No
Exogenous
Constraints
OK?
Yes
Choice